World Bank Group
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The World Bank Group has set two goals for the world to achieve by 2030: (i) End extreme poverty by decreasing the percentage of people living on less than $1.90 a day to no more than 3%, and (ii) promote shared prosperity by fostering the income growth of the bottom 40% for every country. The World Bank is a vital source of financial and technical assistance to developing countries around the world.
Available DatasetsShowing 10 of 10 results
- Data repository for solar and meteorological ground measurements from a network of weather stations in West Africa. The data is provided in the framework of the West African Power Pool project: "Solar Development in Sub-Saharan Africa - Solar resource measurement campaign in West Africa”. Funding is provided by World Bank. Measurement Date Range: - Sunyani: 2021-11-30 – 2023-11-29 - Navrongo: 2021-11-16 – 2023-11-151Licence not specified6 months ago
- These raster files show the land cover classification around Harare in 2006 and 2010. The classification results were based on Spot 5 imagery. Land cover classes in the attribute table are as follows: Class 1 Regular Residential (small planned buildings) Class 2- Regular Residential (small unplanned buildings) Class 3 Commercial/Industrial (large buildings) Class 4 Natural (Vegetation/Soil/non built-up This dataset is part of a paper which illustrates how the capabilities of GIS and satellite imagery can be harnessed to explore and better understand the urban form of several large African cities (Addis Ababa, Nairobi, Kigali, Dar es Salaam, and Dakar). To allow for comparability across very diverse cities, this work looks at the above mentioned cities through the lens of several spatial indicators and relies heavily on data derived from satellite imagery. First, it focuses on understanding the distribution of population across the city, and more specifically how the variations in population density could be linked to transportation. Second, it takes a closer look at the land cover in each city using a semi-automated texture based land cover classification that identifies neighborhoods that appear more regular or irregularly planned. Lastly, for the higher resolution images, this work studies the changes in the land cover classes as one moves from the city core to the periphery. This work also explored the classification of slightly coarser resolution imagery which allowed analysis of a broader number of cities, sixteen, provided the lower cost. When using this dataset keep in mind: Accuracy is higher in closer to the City center, and the distinction between class 1 and class 2 has not been validated, so use with caution. To learn more about the methodology please refer to https://ssrn.com/abstract=28833941Licence not specified8 months ago
- This dataset contains the GIS data used in the report, "Global Photovoltaic Power Potential by Country" generated by Solargis (https://solargis.com), with funding provided by the Energy Sector Management Assistance Program (ESMAP). The study summarizes global solar resource and PV power potential on a country and regional basis. Analysis is based on Solargis's high-resolution datasets, and GIS mask layers which are downloadable via the 'resources' tab. A country comparison spreadsheet is also provided as an additional download, which provides indicators of PV power potential for all countries as described in the study. The study provides: • Ranking and comparison of countries and regions according to their PV potential; • Approximate levelized cost of electricity (LCOE) relevant to current PV projects; • Cross-correlation with the socio-economic indicators relevant to PV development. Data information: Format: raster (GeoTIFF) size: 5.3 GB Zip file contains README.txt1Licence not specified8 months ago
- This raster file represents a land cover classification in Chinandega based off satellite imagery from November 25, 2013. This land cover classification was used in the following report: Mapping Land Cover in Secondary Cities in Central America. This work was initiated as an analytical effort to fill a gap on spatial form of secondary cities. While this is an independent output, this work is tightly linked to the work done under the Central America Urbanization Review. The analysis here described, was used as an input in the definition of urban agglomerations used in the Urbanization Review. The detailed analysis on secondary cities is seen as a complement to the work carried out in the Urbanization Review, in that it zooms into what is happening within a set of cities. The Urbanization Review instead provides a broader look at the system of cities in Central America, highlighting the key bottlenecks the regions faces in moving toward more inclusive, productive, and resilient cities. The Urbanization Review can be found here: http://documents.worldbank.org/curated/en/134151467994680764/pdf/106268-...1Licence not specified8 months ago
- This dataset is the result of a geospatial model that simulates how individuals and traded goods are moved in COD, taking both roads and navigable rivers into account. The Highway Development and Management Model was used to estimate the cost of the road network. The points of origin for the analysis were created by dividing the territory into more than 27,000 cells of approximately 10 kilometers on a side and estimating their centroid. Then, transport cost to the local market was estimated by calculating every possible transport route from every cell centroid to every possible market, and selecting the cheapest route-market combination as the most likely route to a destination. Full report here: http://documents.worldbank.org/curated/en/952691468195575937/Economic-bo...1Licence not specified8 months ago
- The tropical cyclonic strong wind and storm surge model use information from 2594 historical tropical cyclones, topography, terrain roughness, and bathymetry. The historical tropical cyclones used in GAR15 cyclone wind and storm surge model are from five different oceanic basins: Northeast Pacific, Northwest Pacific, South Pacific, North Indian, South Indian and North Atlantic and the tracks were obtained from the IBTrACS database (Knapp et al. 2010). This database represents the repository of information associated with tropical cyclones that is the most up to date. Topography was taken from the Shuttle Radar Topography Mission (SRTM) of NASA, which provides terrain elevation grids at a 90 meters resolution, delivered by quadrants over the world. To account for surface roughness, polygons of urban areas worldwide were obtained from the Socioeconomic Data and Applications Centre, SEDAC (CIESIN et al., 2011). This was considered a good proxy of the spatial variation of surface roughness. A digital bathymetry model is employed with a spatial resolution of 30 arc-seconds, taken from the GEBCO_08 (General Bathymetric Chart of the Oceans) Grid Database of the British Oceanographic Data Centre (2009). Bathymetry is the information about the underwater floor of the ocean having direct influence on the formation of the storm surge. More information about the cyclone wind and storm surge hazard can be found in CIMNE et al., 2015a. Hazard analysis was performed using the software CAPRA Team Tropical Cyclones Hazard Modeler (Bernal, 2014). The vulnerability models used in the risk calculation for GAR correlate loss to the wind speed for 3-seconds gusts. For GAR15, the risk was calculated with the CAPRA-GIS platform which is risk modelling tool of the CAPRA suite (www.ecapra.org). The risk assessment was also conducted by CIMNE and Ingeniar to produced AAL and PML values for cyclone risk.1Licence not specified8 months ago
- Land cover/land use (LULC) maps for the catchments of Kelani Ganga and Attanagalu Oya, and LULC Change comparing 1991, 2001 with the recent LCLU (2012). Classification includes two thematic levels (national 7-class scheme and 15 land cover/land use classes according to user definitions). This dataset is one of the products produced under the 2014-2016 World Bank (WBG) European Space Agency (ESA) partnership, and is published in the partnership report: Earth Observation for Sustainable Development, June 2016.1Licence not specified8 months ago
- Probabilistic tsunami hazard assessment for Indonesia, cnducted by Geoscience Australia the first nationally consistent probabilistic tsunami hazard assessment (PTHA) for Indonesia. This assessment produces time-independent forecasts of tsunami hazards at the coast using data from tsunami generated by local, regional and distant earthquake sources. The methodology is based on the established monte carlo approach to probabilistic seismic hazard assessment (PSHA) and has been adapted to tsunami. We account for sources of epistemic and aleatory uncertainty in the analysis through the use of logic trees and sampling probability density functions. For short return periods (100 years) the highest tsunami hazard is the west coast of Sumatra, south coast of Java and the north coast of Papua. For longer return periods (500–2500 years), the tsunami hazard is highest along the Sunda Arc, reflecting the larger maximum magnitudes. The annual probability of experiencing a tsunami with a height of > 0.5 m at the coast is greater than 10 % for Sumatra, Java, the Sunda islands (Bali, Lombok, Flores, Sumba) and north Papua. The annual probability of experiencing a tsunami with a height of > 3.0 m, which would cause significant inundation and fatalities, is 1–10 % in Sumatra, Java, Bali, Lombok and north Papua, and 0.1–1 % for north Sulawesi, Seram and Flores. The results of this national-scale hazard assessment provide evidence for disaster managers to prioritise regions for risk mitigation activities and/or more detailed hazard or risk assessment. Wave height at coast has been buffered (to overlap onshore admin boundaries), intersected with SRTM topogrpahy, and converted to hazard levels for use in Think Hazard! This was converted by Audrey Hohmann at BRGM.1Licence not specified8 months ago
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- The data on HV lines was obtained from the data collection and mapping work in the WB project "DRC EASE" (PID: P156208). This information was revised and adjusted accordingly after consultations with SNEL and with Energy Specialists working in COD1Licence not specified8 months ago
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